Course description

This in-depth course is designed to take you from beginner to job-ready data scientist. Covering core skills in Python, statistics, machine learning, data preprocessing, visualization, and more—this course is built with today’s data-driven industry in mind.

You’ll work on real-world datasets, build models, and apply algorithms using libraries like Pandas, Scikit-learn, NumPy, Matplotlib, and Seaborn. By the end, you’ll be prepared for careers in Data Science, ML Engineering, or AI Development.

Course Curriculum

Module 1: Introduction to Data Science

• What is data science?

• Data science vs data analytics vs AI

• Data science workflow

Module 2: Python for Data Science

• Python fundamentals (data types, loops, functions)

• Working with Jupyter Notebooks

• Libraries: NumPy, Pandas

• Data manipulation and analysis

Module 3: Statistics & Probability for Data Science

• Descriptive and inferential statistics

• Probability theory

• Distributions and sampling

• Hypothesis testing and confidence intervals

Module 4: Data Cleaning and Preprocessing

• Handling missing values and outliers

• Feature engineering and selection

• Data scaling and encoding techniques

• Using Sklearn’s preprocessing tools

Module 5: Data Visualization

• Using Matplotlib and Seaborn

• Correlation analysis

• Pair plots, heatmaps, histograms

• Storytelling with data

Module 6: Exploratory Data Analysis (EDA)

• Patterns and trends

• Grouping and aggregating data

• Business-focused EDA case study

Module 7: Machine Learning – Supervised Learning

• Introduction to ML and Scikit-learn

• Regression (Linear, Ridge, Lasso)

• Classification (Logistic Regression, KNN, Decision Trees)

• Model evaluation: accuracy, precision, recall, ROC-AUC

Module 8: Machine Learning – Unsupervised Learning

• Clustering (K-Means, Hierarchical)

• Dimensionality reduction (PCA)

• Anomaly detection

Module 9: Advanced Topics

• Time Series Analysis (forecasting, ARIMA)

• Introduction to Deep Learning

• Working with TensorFlow/Keras (optional)

• Natural Language Processing basics

Module 10: Real-World Projects

• Project 1: Customer Segmentation

• Project 2: Predicting House Prices

• Project 3: Sentiment Analysis

• Project 4: Sales Forecasting

Module 11: Resume & Interview Prep

• Building a portfolio with GitHub

• Data science resume and LinkedIn optimization

• Interview questions and mock test

What will i learn?

• Analyze and visualize complex data
• Build predictive ML models using Python
• Apply statistical reasoning to real-world problems
• Create a portfolio of data science projects

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